Joint planning method for cross-domain unmanned swarm target assignment and mission trajectory

doi:10.23919/JSEE.2025.000073

Journal of Systems Engineering and Electronics ›› 2025, Vol. 36 ›› Issue (3): 736-753.doi: 10.23919/JSEE.2025.000073

• SYSTEMS ENGINEERING • Previous Articles

Joint planning method for cross-domain unmanned swarm target assignment and mission trajectory

Ning WANG¹^,²(), Xiaolong LIANG¹^,²^,*(), Zhe LI¹^,²(), Yueqi HOU¹^,²(), Aiwu YANG¹^,²()

¹ Air Traffic Control and Navigation School, Air Force Engineering University, Xi’an 710051, China
² Shaanxi Province Laboratory of Meta-synthesis for Electronic & Information System, Air Force Engineering University, Xi’an 710051, China

Received:2023-09-05 Online:2025-06-18 Published:2025-07-10
Contact: Xiaolong LIANG E-mail:wnlearning@163.com;lxllearning@163.com;kongyanshi@126.com;afeu_hyq@163.com;ai_five@163.com
About author:
WANG Ning was born in 1997. He received his M.S. degree from the Air Force Engineering University in Xi’an, China, in 2021. He is currently pursuing his Ph.D. degree with the Air Force Engineering University, Xi’an, China. His research interests include intelligent decision making and cooperative control of cross-domain unmanned swarms. E-mail: wnlearning@163.com

LIANG Xiaolong was born in 1981. He received his Ph.D. degree from Air Force Engineering University, Xi’an, China, in 2010. He is currently a professor with Air Traffic Control and Navigation School, Air Force Engineering University. His research interests include aircraft swarm technology, airspace management intelligence, and intelligent aviation system. E-mail: lxllearning@163.com

LI Zhe was born in 1981. He received his M.S. degree from Air Force Engineering University, Xi’an, China, in 2007. He is currently an associate professor with Air Traffic Control and Navigation School, Air Force Engineering University. His research interests include aircraft swarm technology and artificial intelligence. E-mail: kongyanshi@126.com

HOU Yueqi was born in 1995. He received his Ph.D. degree from Air Traffic Control and Navigation School, Air Force Engineering University, Xi’an, China, in 2023. His research interests include deep reinforcement learning, multi-agent reinforcement learning, and intelligent decision-making with applications in air confrontation of aircraft swarm. E-mail: afeu_hyq@163.com

YANG Aiwu was born in 1996. He received his Ph.D. degree from Air Traffic Control and Navigation School, Air Force Engineering University, Xi’an, China, in 2024. His research interests include distributed control and optimization with application in multi-agent systems, and unmanned autonomous systems. E-mail: ai_five@163.com

Abstract

Abstract:

Compared with single-domain unmanned swarms, cross-domain unmanned swarms continue to face new challenges in terms of platform performance and constraints. In this paper, a joint unmanned swarm target assignment and mission trajectory planning method is proposed to meet the requirements of cross-domain unmanned swarm mission planning. Firstly, the different performances of cross-domain heterogeneous platforms and mission requirements of targets are characterised by using a collection of operational resources. Secondly, an algorithmic framework for joint target assignment and mission trajectory planning is proposed, in which the initial planning of the trajectory is performed in the target assignment phase, while the trajectory is further optimised afterwards. Next, the estimation of the distribution algorithms is combined with the genetic algorithm to solve the objective function. Finally, the algorithm is numerically simulated by specific cases. Simulation results indicate that the proposed algorithm can perform effective task assignment and trajectory planning for cross-domain unmanned swarms. Furthermore, the solution performance of the hybrid estimation of distribution algorithm (EDA)-genetic algorithm (GA) algorithm is better than that of GA and EDA.

Key words: cross-domain swarm, unmanned system, target assignment, trajectory planning, joint planning, hybrid estimation of distribution algorithm (EDA)-genetic algorithm (GA)

Ning WANG, Xiaolong LIANG, Zhe LI, Yueqi HOU, Aiwu YANG. Joint planning method for cross-domain unmanned swarm target assignment and mission trajectory[J]. Journal of Systems Engineering and Electronics, 2025, 36(3): 736-753.

Figures/Tables 24

Fig 1

Fig 2

Fig 3

Fig 4

Fig 5

Fig 6

Fig 7

Table 1

Table 2

Fig 8

Table 3

Table 4

Table 5

Calculation results of the sample target allocation"

Platform	Mission sequence	Track length/km	Time/s
${S_1}$	${T_2}$	63.40	6340.35
${S_2}$	${T_{26}} \Rightarrow {T_{16}} \Rightarrow {T_{27}} \Rightarrow {T_{36}} \Rightarrow {T_{38}}$	86.38	2879.32
${S_3}$	${T_8} \Rightarrow {T_{28}} \Rightarrow {T_{18}} \Rightarrow {T_7}$	115.61	7707.55
${S_4}$	${T_{23}} \Rightarrow {T_{18}}$	40.71	1356.85
${S_5}$	${T_{12}} \Rightarrow {T_{21}} \Rightarrow {T_{28}} \Rightarrow {T_{25}} \Rightarrow {T_{11}} \Rightarrow {T_{29}} \Rightarrow {T_6} \Rightarrow {T_{13}}$	180.36	12024.00
${S_6}$	${T_5} \Rightarrow {T_{30}} \Rightarrow {T_{26}} \Rightarrow {T_{20}} \Rightarrow {T_{34}}$	113.93	3797.66
${S_7}$	${T_{19}} \Rightarrow {T_{15}} \Rightarrow {T_{27}} \Rightarrow {T_{12}} \Rightarrow {T_{36}}$	119.01	7933.96
${S_8}$	${T_{26}} \Rightarrow {T_{24}} \Rightarrow {T_{27}} \Rightarrow {T_{12}}$	74.48	2482.52
${S_9}$	${T_2} \Rightarrow {T_9} \Rightarrow {T_{30}} \Rightarrow {T_{40}} \Rightarrow {T_{20}} \Rightarrow {T_{33}}$	132.32	8821.44
${S_{10}}$	${T_{28}} \Rightarrow {T_{17}} \Rightarrow {T_8}$	101.42	3380.61
${S_{11}}$	${T_{38}} \Rightarrow {T_{21}} \Rightarrow {T_{25}} \Rightarrow {T_{18}}$	91.26	9125.70
${S_{12}}$	${T_8} \Rightarrow {T_{32}} \Rightarrow {T_{36}} \Rightarrow {T_{10}} \Rightarrow {T_{19}} \Rightarrow {T_{30}} \Rightarrow {T_{21}}$	117.43	11743.00
${S_{13}}$	${T_{18}} \Rightarrow {T_{27}} \Rightarrow {T_{13}} \Rightarrow {T_3} \Rightarrow {T_5} \Rightarrow {T_{39}}$	113.73	11373.45
${S_{14}}$	${T_3} \Rightarrow {T_9} \Rightarrow {T_{25}} \Rightarrow {T_{38}}$	96.72	3223.98
${S_{15}}$	${T_{32}}$	85.83	2861.02
${S_{16}}$	${T_{20}} \Rightarrow {T_{35}} \Rightarrow {T_6} \Rightarrow {T_{13}} \Rightarrow {T_{16}}$	93.21	3106.97
${S_{17}}$	${T_{26}} \Rightarrow {T_{37}} \Rightarrow {T_{39}} \Rightarrow {T_9} \Rightarrow {T_{17}} \Rightarrow {T_4}$	172.50	11500.04
${S_{18}}$	${T_{11}} \Rightarrow {T_{35}}$	72.40	2413.25
${S_{19}}$	${T_{35}} \Rightarrow {T_{38}} \Rightarrow {T_8} \Rightarrow {T_6}$	76.56	2551.89
${S_{20}}$	${T_{11}} \Rightarrow {T_{31}} \Rightarrow {T_{23}} \Rightarrow {T_{10}} \Rightarrow {T_{22}} \Rightarrow {T_5} \Rightarrow {T_{34}}$	181.44	12096.15
${S_{21}}$	${T_{38}} \Rightarrow {T_{14}} \Rightarrow {T_{22}} \Rightarrow {T_3}$	98.61	3287.00
${S_{22}}$	${T_8} \Rightarrow {T_{25}} \Rightarrow {T_{32}}$	80.65	2688.34
${S_{23}}$	${T_{14}} \Rightarrow {T_{17}}$	78.18	7818.08
${S_{24}}$	${T_{32}} \Rightarrow {T_{31}} \Rightarrow {T_{10}}$	93.38	3112.60
${S_{25}}$	${T_1} \Rightarrow {T_{24}} \Rightarrow {T_{36}} \Rightarrow {T_{11}} \Rightarrow {T_{10}}$	75.57	2518.96

Table 5

Fig 9

Fig 10

Fig 11

Fig 12

Fig 13

Table 6

Calculation results of the sample trajectory optimisation"

Platform	Mission sequence	Track length/km	Time/s
${S_1}$	${T_2}$	63.40	6340.35
${S_2}$	${T_{38}} \Rightarrow {T_{36}} \Rightarrow {T_{27}} \Rightarrow {T_{16}} \Rightarrow {T_{26}}$	86.38	2879.32
${S_3}$	${T_7} \Rightarrow {T_{28}} \Rightarrow {T_{18}} \Rightarrow {T_8}$	111.05	7403.41
${S_4}$	${T_{18}} \Rightarrow {T_{23}}$	40.71	1356.85
${S_5}$	${T_{13}} \Rightarrow {T_{29}} \Rightarrow {T_{28}} \Rightarrow {T_{25}} \Rightarrow {T_{21}} \Rightarrow {T_{11}} \Rightarrow {T_6} \Rightarrow {T_{12}}$	133.52	8901.37
${S_6}$	${T_{34}} \Rightarrow {T_{20}} \Rightarrow {T_{26}} \Rightarrow {T_{30}} \Rightarrow {T_5}$	113.93	3797.66
${S_7}$	${T_{15}} \Rightarrow {T_{27}} \Rightarrow {T_{12}} \Rightarrow {T_{36}} \Rightarrow {T_{19}}$	110.06	7337.62
${S_8}$	${T_{27}} \Rightarrow {T_{12}} \Rightarrow {T_{26}} \Rightarrow {T_{24}}$	62.09	2069.82
${S_9}$	${T_{30}} \Rightarrow {T_{33}} \Rightarrow {T_{20}} \Rightarrow {T_{40}} \Rightarrow {T_2} \Rightarrow {T_9}$	130.52	8701.08
${S_{10}}$	${T_{28}} \Rightarrow {T_{17}} \Rightarrow {T_8}$	101.42	3380.61
${S_{11}}$	${T_{38}} \Rightarrow {T_{21}} \Rightarrow {T_{25}} \Rightarrow {T_{18}}$	91.26	9125.70
${S_{12}}$	${T_8} \Rightarrow {T_{32}} \Rightarrow {T_{36}} \Rightarrow {T_{10}} \Rightarrow {T_{19}} \Rightarrow {T_{30}} \Rightarrow {T_{21}}$	117.43	11743.00
${S_{13}}$	${T_5} \Rightarrow {T_{39}} \Rightarrow {T_3} \Rightarrow {T_{27}} \Rightarrow {T_{13}} \Rightarrow {T_{18}}$	110.25	11024.97
${S_{14}}$	${T_{25}} \Rightarrow {T_9} \Rightarrow {T_{38}} \Rightarrow {T_3}$	84.46	2815.50
${S_{15}}$	${T_{32}}$	85.83	2.86
${S_{16}}$	${T_{20}} \Rightarrow {T_{16}} \Rightarrow {T_{13}} \Rightarrow {T_{35}} \Rightarrow {T_6}$	89.35	2978.27
${S_{17}}$	${T_4} \Rightarrow {T_9} \Rightarrow {T_{39}} \Rightarrow {T_{37}} \Rightarrow {T_{17}} \Rightarrow {T_{26}}$	151.65	10109.69
${S_{18}}$	${T_{11}} \Rightarrow {T_{35}}$	72.40	2413.25
${S_{19}}$	${T_6} \Rightarrow {T_8} \Rightarrow {T_{35}} \Rightarrow {T_{38}}$	75.91	2530.46
${S_{20}}$	${T_{23}} \Rightarrow {T_{31}} \Rightarrow {T_5} \Rightarrow {T_{34}} \Rightarrow {T_{22}} \Rightarrow {T_{10}} \Rightarrow {T_{11}}$	124.83	8322.00
${S_{21}}$	${T_{38}} \Rightarrow {T_3} \Rightarrow {T_{22}} \Rightarrow {T_{14}}$	97.86	3262.05
${S_{22}}$	${T_{25}} \Rightarrow {T_8} \Rightarrow {T_{32}}$	78.29	2609.75
${S_{23}}$	${T_{14}} \Rightarrow {T_{17}}$	78.18	7818.08
${S_{24}}$	${T_{10}} \Rightarrow {T_{31}} \Rightarrow {T_{32}}$	93.38	3112.60
${S_{25}}$	${T_1} \Rightarrow {T_{24}} \Rightarrow {T_{36}} \Rightarrow {T_{11}} \Rightarrow {T_{10}}$	75.57	2518.96

Table 6

Fig 14

Fig 15

Fig 16

Fig 17

Fig 18

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Target	Service demand	Position	Requirement complexity
${T_1}$	{0,0,1,0,0}	(24.11,26.67)	1
${T_2}$	{0,1,0,0,0}	(13.29,23.03)	1
${T_3}$	{1,0,1,0,0}	(21.05,9.91)	2
${T_4}$	{0,1,0,1,0}	(37.13,4.41)	2
${T_5}$	{1,1,0,0,1}	(11.45,22.48)	3
${T_6}$	{1,1,0,1,0}	(44.91,7.86)	3
${T_7}$	{1,1,1,1,0)	(28.48,5.23)	4
${T_8}$	{1,1,1,0,1}	(30.56,23.95)	4
${T_9}$	{1,2,1,1,0}	(11.35,24.21)	5
${T_{10}}$	{1,1,2,1,0}	(31.63,2.05)	5
${T_{11}}$	{2,2,1,0,1}	(32.65,10.17)	6
${T_{12}}$	{1,2,2,1,0}	(46.19,9.45)	6
${T_{13}}$	{0,0,0,0,1}	(52.52,14.72)	1
${T_{14}}$	{1,0,1,0,0}	(44.53,8.82)	2
${T_{15}}$	{0,1,0,1,1}	(55.76,33.87)	3
${T_{16}}$	{0,0,1,0,0}	(55.95,15.44)	1
${T_{17}}$	{0,0.0,0,0}	(57.1,37.21)	0
${T_{18}}$	{0,0.1,0.1}	(26.96.28.77)	2
${T_{19}}$	{1,1,0,0,0}	(30.23,14.84)	2
${T_{20}}$	{1,1,0,0,1}	(48.14,7.05)	3
${T_{21}}$	{0,0,1,0,0}	(5.27,15.65)	1
${T_{22}}$	{0,0,1,1,1}	(26.96,4.47)	3
${T_{23}}$	{0,0,0,1,1}	(39.92,39.66)	2
${T_{24}}$	{0,0,1,0,0)	(33.37,26.74)	1
${T_{25}}$	{0,1,0,1,1}	(13.06,29.17)	3
${T_{26}}$	{1,0,1,0,0)	(44.84,17.94)	2
${T_{27}}$	{0,1,0,0,0}	(55.46,14.15)	1
${T_{28}}$	{0,0,1,0,1}	(12.25,36.42)	2
${T_{29}}$	{0,1,0,1,0}	(38.9,36.55)	2
${T_{30}}$	{1,1,1,1,1}	(21.36,18.44)	5
${T_{31}}$	{1,0,1,11}	(27.96;38.55)	4
${T_{32}}$	{0,0,1,0,1}	(50.35,25.4)	2
${T_{33}}$	{1,1,1,0,0}	(45.82,0.57)	3
${T_{34}}$	{1,1,0,1,1}	(1.74,4.17)	4
${T_{35}}$	{1,0,0,0,0}	(19.05,13.14)	1
${T_{36}}$	{1,1,0,0,1}	(37.28,13.16)	3
${T_{37}}$	{0,0,0,1,1}	(0.28,33.45)	2
${T_{38}}$	{0,0,1,1,1}	(17.76,13.52)	3
${T_{39}}$	{0,1,0,1,0}	(4.99,20.29)	2
${T_{40}}$	{0,1,0,0,1}	(52.74,37.08)	2

Platform	Service provision	Position	Platform category
${S_1}$	{0 0,0,2,2}	(38.89,4.34)	UUV
${S_2}$	{2,0,0,0,0}	(37.3,27.26)	UAV
${S_3}$	{1,1,2,1,2}	(54.46, 15.65)	USV
${S_4}$	{2 0,0,0,0}	(39.35,28.31)	UAV
${S_5}$	{1,1,2,1,2}	(50.53,5.21)	USV
${S_6}$	{2 0,0,1,0}	(4.21,16.25)	UAV
${S_7}$	{1,1,0,1,2}	(13.62,26.46)	USV
${S_8}$	{2,0,0,0,0}	(50.13, 24.62)	UAV
${S_9}$	{1,1,2,1,2}	(11.98, 15.3)	USV
${S_{10}}$	{2,0,0,1,0}	(16.82, 24.09)	UAV
${S_{11}}$	{0,0,0,0,2}	(36.61,3.02)	UUV
${S_{12}}$	{0 0,0,0,2}	(24.24, 26.24)	UUV
${S_{13}}$	{0,0,0,2,0}	(22.34,24.31)	UUV
${S_{14}}$	{2,0,0,0,0}	(46.18,22.44)	UAV
${S_{15}}$	{2,0,0,1,0}	(12.01,6.12)	UAV
${S_{16}}$	{2,0,1,0,0}	(48.1,0.55)	UAV
${S_{17}}$	{1,1,2,1,2}	(46.05,11.61)	USV
${S_{18}}$	{2,0,0,1,0}	(49.5, 28.06)	UAV
${S_{19}}$	{2 0,0,1,0}	(16.49,4.97)	UAV
${S_{20}}$	{1,1,0,1,2}	(36.29,22.81)	USV
${S_{21}}$	{2,0,1,0, 0}	(0.64,24.39)	UAV
${S_{22}}$	{2 0,0,0,0}	(29.91,20.19)	UAV
${S_{23}}$	{0,0,0,2,0}	(59.36, 5.38)	UUV
${S_{24}}$	{2,0,1,0,0}	(50.92,24.0)	UAV
${S_{25}}$	{2,0,1,0,0}	(13.14,19.1)	UAV

Platform category	Model	Optical infrared detection	Interference	Anti-aircraft attack	Anti-surface attack	Anti-underwater attack
UAV	1	√√	×	×	×	×
UAV	2	√√	×	√	×	×
UAV	3	√√	×	×	√	×
USV	1	√	√	×	√	√√
USV	2	√	√	√√	√	×
USV	3	√	√	√√	√	√√
UUV	1	×	×	×	×	√√
UUV	2	×	×	×	√√	×
UUV	3	×	×	×	√√	√√

Parameter	Value	Parameter	Value
${N_T}$	40	${N_S}$	25
$\omega _1^t$	1	$\omega _2^t$	1
$\omega _3^t$	3	$r$	$2 \times {10^4}$
$\theta $	$2 \times {10^4}$	${\rm{NP}}$	500
${P_c}$	0.4	${P_m}$	0.2
${G_{{\rm{end}}} }$	1000	${G_{{\rm{ph}}} }$	400
${\omega _1}$	0.4	${\omega _2}$	0.6
${P_{m2}}$	0.1	${\rm{NP2}}$	200
${P_{c2}}$	0.5	${G_{{\rm{end2}}} }$	500

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Joint planning method for cross-domain unmanned swarm target assignment and mission trajectory

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